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TESTc0b9c1d124sklearn.svm._classes.SVC

TESTc0b9c1d124sklearn.svm._classes.SVC

Visibility: public Uploaded 18-10-2024 by Continuous Integration sklearn==1.5.2 numpy>=1.19.5 scipy>=1.6.0 joblib>=1.2.0 threadpoolctl>=3.1.0 0 runs
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  • openml-python python scikit-learn sklearn sklearn_1.5.2
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C-Support Vector Classification. The implementation is based on libsvm. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of samples. For large datasets consider using :class:`~sklearn.svm.LinearSVC` or :class:`~sklearn.linear_model.SGDClassifier` instead, possibly after a :class:`~sklearn.kernel_approximation.Nystroem` transformer or other :ref:`kernel_approximation`. The multiclass support is handled according to a one-vs-one scheme. For details on the precise mathematical formulation of the provided kernel functions and how `gamma`, `coef0` and `degree` affect each other, see the corresponding section in the narrative documentation: :ref:`svm_kernels`. To learn how to tune SVC's hyperparameters, see the following example: :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py`

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